The need for effective surveillance and security at airports, nuclear power plants and other secure locations is more pressing than ever. Organizations responsible for conducting such surveillance typically deploy a plurality of sensors (e.g., closed circuit television video and infrared cameras, radars, etc.) to provide physical security and wide-area awareness. For example, across the United States, an estimated nine million video security cameras are in use.
Typical vision-based surveillance systems depend on low-level video tracking as a means of alerting an operator to an event. If detected motion (e.g., as defined by flow) exceeds a predefined threshold, an alarm is generated. While such systems provide improved performance over earlier pixel-change detection systems, they still tend to exhibit a relatively high false alarm rate. The high false alarm rate is due, in part, to the fact that low-level detection and tracking algorithms do not adapt well to different imager and scene conditions (e.g., the same tracking rules apply in, say, an airport and a sea scene). In addition, the high-level analysis and rule-based systems that post-process the tracking data for decision making (alarm generation) are typically simplistic and fail to reflect many real world scenarios (e.g., a person returning a few feet through an airport exit to retrieve a dropped object will typically trigger an alarm even if the person resumes his path through the exit).
Thus, there is a need in the art for an improved method and apparatus for video surveillance.